Evolution of the Coefficient of Friction with Surface Wear for Advanced Surface Textured Composites
Bibliographic record
Abstract
In this study, textured composite surface with protruding fibers is developed, which exhibits extremely high coefficient of friction on ice. A novel composite material with improved wear resistibility is aimed to determine with the target to maintain its slip‐resistance properties over extended use. Particularly, two thermoplastic elastomers are compared, namely, thermoplastic polyurethanes (TPU) and Styrene–Butadiene–Styrene (SBS), reinforced with five types of fibers with varying stiffnesses and ductility, including alumina, basalt, glass, carbon, and poly(p‐phenylene‐2,6‐benzobisoxazole)) (PBO). The surface science of the composite is analyzed by using Fourier tranorm infrared spectroscopy to assess the intensity of existing interfacial bonding at fiber/matrix interface and scanning electron microscopy imaging for visual characterization. The results show that TPU composites have significantly higher abrasion resistance and slip resistance on ice as compared to SBS composites with the maximum abrasive resistance index (347.5 ± 29.5, p < 0.0001) and coefficient of friction on ice (0.375 ± 0.031, p < 0.0001) for PBO/TPU composite. Similarly, Fourier tranorm infrared spectroscopy spectrum demonstrates stronger existing bands in TPU compared to SBS composites indicative of better fiber wetting in TPU composites. The current PBO–TPU composite can be a potential candidate for various antislip applications as it has improved wear‐resistance (22%) and slip‐resistance (57%) properties, with respect to pure TPU.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".